National University of Malaysia

About: National University of Malaysia is a education organization based out in Kuala Lumpur, Malaysia. It is known for research contribution in the topics: Population & Heat transfer. The organization has 26593 authors who have published 41270 publications receiving 552683 citations. The organization is also known as: NUM & Universiti Kebangsaan Malaysia.

An Internet of Things Based Smart Waste Management System Using LoRa and Tensorflow Deep Learning Model

Abstract: Traditional waste management system operates based on daily schedule which is highly inefficient and costly. The existing recycle bin has also proved its ineffectiveness in the public as people do not recycle their waste properly. With the development of Internet of Things (IoT) and Artificial Intelligence (AI), the traditional waste management system can be replaced with smart sensors embedded into the system to perform real time monitoring and allow for better waste management. The aim of this research is to develop a smart waste management system using LoRa communication protocol and TensorFlow based deep learning model. LoRa sends the sensor data and Tensorflow performs real time object detection and classification. The bin consists of several compartments to segregate the waste including metal, plastic, paper, and general waste compartment which are controlled by the servo motors. Object detection and waste classification is done in TensorFlow framework with pre-trained object detection model. This object detection model is trained with images of waste to generate a frozen inference graph used for object detection which is done through a camera connected to the Raspberry Pi 3 Model B+ as the main processing unit. Ultrasonic sensor is embedded into each waste compartment to monitor the filling level of the waste. GPS module is integrated to monitor the location and real time of the bin. LoRa communication protocol is used to transmit data about the location, real time and filling level of the bin. RFID module is embedded for the purpose of waste management personnel identification.

Observed changes in extreme temperature and precipitation over Indonesia

Abstract: This paper documents the changes in climate extremes over Indonesia during the past three decades (1983–2012) based on a subset of extreme indices recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI). The extreme indices were calculated based on the quality controlled daily observational data (minimum and maximum temperature and precipitation) from 88 weather stations. Overall, we found significant and spatially coherent trends of warming in the temperature indices over Indonesia, consistent with other studies conducted at different countries within the Southeast Asia. The frequency of cool days (TX10p) and cools nights (TN10p) had decreased whereas warm days (TX90p) and warm nights (TN90p) were observed more frequently. Averaged over the country, the annual mean of daily maximum (TXmean) and minimum temperature (TNmean) had increased significantly by 0.18 and 0.30 °C decade−1, respectively. Other temperature indices also showed significant warming trends. In contrast, trends in the precipitation extremes indices were generally not significant and less spatially coherent. However, a tendency towards wetter conditions was observed, in agreement with the results at the global scale. The daily precipitation intensity (SDII) had increased significantly over the country by 0.21 mm day−1 decade−1 during the period studied. At the regional scale, we observed a significant wetting trend in the annual highest daily amount (RX1day) and the rainfall amount contributed by the extremely very wet days (R99p) in the northern part of the country. The wetting trends of a number of extreme precipitation indices were depicted prominently in December–January–February (DJF) and/or March–April–May (MAM) seasons, both at country and regional levels. However, for the southern region of Indonesia, a drying tendency was observed for June–July–August (JJA), September–October–November (SON) and MAM.

Analysis of the Chemical Composition of the Essential Oil of Polygonum minus Huds. Using Two-Dimensional Gas Chromatography-Time-of-Flight Mass Spectrometry (GC-TOF MS)

Abstract: The essential oil in leaves of Polygonum minus Huds., a local aromatic plant, were identified by a pipeline of gas chromatography (GC) techniques coupled with mass-spectrometry (MS), flame ionization detector (FID) and two dimensional gas chromatography time of flight mass spectrometry (GC x GC-TOF MS). A total of 48 compounds with a good match and high probability values were identified using this technique. Meanwhile, 42 compounds were successfully identified in this study using GC-MS, a significantly larger number than in previous studies. GC-FID was used in determining the retention indices of chemical components in P. minus essential oil. The result also showed the efficiency and reliability were greatly improved when chemometric methods and retention indices were used in identification and quantification of chemical components in plant essential oil.